Troubleshooting a vehicle often requires some form of customer service; to help guide a customer towards a resolution. Consumer reporting of faulty vehicular components can be made via a telephone- based service call, or gathered through telemetry via embedded intelligent transportation systems (ITS). During data transmission, free text is created in the dialogue between the customer and the service representative. While free text is generated, key details of the discussion can be extracted and recorded. This paper describes methods used to process the recorded free text data. This method can help classify and direct the call to the correct channel of support tools and resources. An anonymous customer service report consisting of 75,000 calls was used for feature extraction. Five thousands of the calls were used in supervised learning to support data classifications. The matrix of data was evaluated for accuracy and repeated to minimize error and increase accuracy. By incorporating hand-crafted features using domain expertise, our natural language processing (NLP) based approach can achieve 85 percent accuracy in classifying the service calls.


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    Titel :

    Mining Vehicle Failure Consumer Reports for Enhanced Service Efficiency


    Beteiligte:
    Khodadadi, Ali (Autor:in) / Chuah, Chen Nee (Autor:in) / Woo, Sang Hoon (Autor:in) / Dalal, Ashish (Autor:in)


    Erscheinungsdatum :

    01.09.2019


    Format / Umfang :

    297413 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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